Long-horizon compositional manipulation has become increasingly important for real-world robot deployment, where a single task involves multiple coordinated subtasks. Existing world-action models (WAMs) jointly predict short-horizon visual futures and actions, but typically lack explicit subtask-level reasoning. We pro...
Shu-Kai Gong, Xuan-Ran Zhai, Yintianrun Zhang et al.· 0 citations
Vision-Language-Action (VLA) models have become a powerful paradigm for robot manipulation, but training a single generalist policy for heterogeneous robot embodiments remains an open problem. Existing methods have two main limitations. First, they underuse dynamics priors shared across diverse visual and interaction d...
Jun-Feng Li, Junjie He, Zhi-De Zhong et al.· 1 citation
4D-WAM is proposed, a model-agnostic training strategy that injects spatiotemporal knowledge from 3D trajectory fields into WAMs through representation alignment, enabling WAMs to learn trajectory-level spatiotemporal representations.
Lishan Yang, Wen-Xuan Song, Xi Wang et al.· 5 citations· ⚡1
Autonomous exploration in large-scale and cluttered environments remains a severe challenge for uncrewed aerial vehicles (UAVs). Existing methods still suffer from high computational costs, long-distance revisits, and discontinuous flight. To address these issues, we propose PACE, a hierarchical exploration framework d...
Yuxiang Gao, Zhuoxuan Wang, Xian-Lu Tao et al.· IEEE Robotics and Automation...· 0 citations
PSG-JEPA is proposed, a physically grounded JEPA world model that shapes its latent space with two complementary grounding objectives beyond forward prediction: grounding individual latents in robot proprioceptive state, and grounding latent pairs in multi-horizon joint-angle changes.
